Dr. Junmei Wang is an Associate Professor of Pharmaceutical Sciences and a member of the Computational Chemical Genomics Screening Center (www.CBLigand.org/CCGS), University of Pittsburgh School of Pharmacy. Dr. Wang received his PhD from Peking University in China, and his was trained as a postdoctoral associate with Dr. Peter Kollman at University of California San Francisco. Before joining University of Pittsburgh, Dr. Wang was an associate professor at University of Texas Southwestern Medical Center. Dr. Wang is also a long-term active developer of the Amber force field and software (www.ambermd.org). He and other collaborators developed a set of popular AMBER force fields, including FF99, GAFF/GAFF2 and polarizable FF based on Thole's dipole-interaction models as well as the Antechamber module implemented in AMBER software packages.
- PhD from Peking University in China
Education & Training
Computational Chemistry and Biophysics, Drug Discovery and Development, Systems Pharmacology and Outcomes, specifically
1. Molecular mechanics force field (MMFF)
2. MMFF-based scoring function
3. computer-aided drug design
4. Molecular simulations of biomolecular systems and processes
5. Machine learning and AI in biomedical research
6. Computational systems pharmacology and pharmacometrics
7. PK/PD modeling and simulations
2022 Certara/SimCYP 2022 Academic Award's for 'Most-Effective Teaching Application'
Year 2023
1. Sun, J.; Wan, Z.; Huang, H.; West III, R. E.; Zhang, M.; Zhang, B.; Cai, X.; Zhang, Z.; Luo, Z.; Chen Y.; Zhang, Y.; Xie, W.; Yang, D.; Nolin, T.; Wang, J. M.; Li, S. Overcoming Pancreatic Cancer Immune Resistance by Codelivery of CCR2 Antagonist Using a STING-Activating Gemcitabine-Based Nanocarrier. Materials Today, 2023, 62, 33-50.
2. Wang, J. M.; Blount, P. Feeling the tension: the bacterial mechanosensitive channel of large conductance as a model system and drug target. Current Opinion in Physiology, 2023, 31, 100627.
3. He, X.; Man, V. H.; Gao, J.; Wang, J. M.* Investigation of the Structure of Full-Length Tau Proteins with Coarse-Grained and All-Atom Molecular Dynamics Simulations. ACS Chemical Neuroscience, 2023, 14, 2, 209–217.
4. Man, V. H.;* He, X.; Gao, J.; Wang, J. M.* Phosphorylation of Tau R2 Repeat Destabilizes Its Binding to Microtubules: A Molecular Dynamics Simulation Study. ACS Chemical Neuroscience, 2023, 14, 3, 458–467.
5. Man, V. H.;* He, X.; Han, F.; Cai, L.; Wang, L.; Niu, T.; Zhai, J.; Ji, B.; Gao, J.; Wang, J. M.* Phosphorylation at Ser289 Enhances the Oligomerization of Tau Repeat R2. Journal of Chemical Information & Modeling, 2023, 63, 4, 1351–1361.
6. Sun, Y.; He, X.; Hou, T.; Cai, L.; Man, V. H.; Wang, J. M.* Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method. Journal of Computational Chemistry, 2023, 2023, 44,1334-1346.
7. Sun, Y.; He, X.; Hou, T.; Cai, L.; Man, V. H.; Wang, J. M.* Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol–water partition coefficient (logP) for druglike molecules using MM-PBSA method. Journal of Computational Chemistry, 2023, 44(13):1300-1311.
8. Man, H. M.*; He, X.; Nguyen, P. H.; Sagui, C.; Roland, C.; Xie, X.-Q.; Wang, J. M.* Unpolarized laser method for infrared spectrum calculation of amide I Cdouble bondO bonds in proteins using molecular dynamics simulation. Computers in Biology and Medicine, 2023, 106902.
9. Wang, J.; Zeng, Y.; Sun, H.; Wang, J. M.; Wang, X.; Jin, R.; Wang, M.; Zhang, X.; Cao, D.; Chen, X.; Hsieh, C.-Y.; Hou, T. Molecular Generation with Reduced Labeling through Constraint Architecture. Journal of Chemical Information and Modeling, 2023, In Press.
Year 2022
1. He, X.; Walker, B.; Man, V. H.; Ren, P.*; Wang, J. M.#*, Recent progress in general force fields of small molecules. Curr Opin Struct Biol 2022, 72, 187-193.
2. Wei, H.; Duan, Y.; Wang, J. M.; Cieplak, P.; Luo, R., Development of polarizable Gaussian multipole model. Biophysical Journal 2022, 121, 157a.
3. Yuan, J. Y.; Jiang, C.; Wang, J. M.; Chen, C. J.; Hao, Y. X.; Zhao, G. Y.; Feng, Z. W.; Xie, X. Q., In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators. Molecules 2022, 27 (2).
4. Zhai, J. C.; He, X. B.; Man, V. H.; Sun, Y. C.; Ji, B. H.; Cai, L. J.; Wang, J. M.#*, A multiple-step in silico screening protocol to identify allosteric inhibitors of Spike-hACE2 binding. Physical Chemistry Chemical Physics 2022, 24 (7), 4305-4316
5. Strand, A.; Shen, S. T.; Tomchick, D. R.; Wang, J. M.; Wang, C. R.; Deisenhofer, J., Structure and dynamics of major histocompatibility class Ib molecule H2-M3 complexed with mitochondrial-derived peptides. Journal of Biomolecular Structure & Dynamics 2021, In Press. (10.1080/07391102.2021.1942214)
6. Hao, D. X.; He, X. B.; Roitberg, A. E.; Zhang, S. L.; Wang, J. M.#*, Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials. Journal of Chemical Theory and Computation 2022, 18, 978-991.
7. Zhai, J. C.; Ji, B. H.; Liu, S. H.; Zhang, Y. Z.; Cai, L. J.; Wang, J. M.#*, In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data. European Journal of Drug Metabolism and Pharmacokinetics 2022, 47, 403-417.
8. Nguyen, H. L.; Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J. M.; Nguyen, P. H., Elastic moduli of normal and cancer cell membranes revealed by molecular dynamics simulations. Physical Chemistry Chemical Physics 2022, 24, 6225-6237.
9. Wray R.; Blount P.;* Wang, J. M.;* Iscla, I.* In Silico Screen Identifies a New Family of Agonists for the Bacterial Mechanosensitive Channel MscL. Antibiotics 2022, 11(4), 433; https://doi.org/10.3390/antibiotics11040433.
10. Zhai J.; Ji, B.; Cai, L.; Liu, S.; Sun, Y.; Wang, J. M.#* Physiologically-Based Pharmacokinetics Modeling for Hydroxychloroquine as a Treatment for Malaria and Optimized Dosing Regimens for Different Populations. Journal of Personalized Medicine, 2022, 12(5), 796.
11. Man, V. H.; Lin, D.; He, X. B.; Gao, J.;* Wang, J. M.#*, Joint Computational/Cell-based Oligomerization for Screening Inhibitors of Tau Assembly: A Proof-of-Concept Study. Journal of Alzeheimer’s Disease 2022, 89(1), 107-119.
12. Zhai, J.; He, X.; Sun, Y.; Wan, Z.; Ji, B.; Liu, S.; Li, S.; Wang, J. M.#* In Silico Binding Affinity Prediction for Metabotropic Glutamate Receptors Using Both Endpoint Free Energy Methods and A Machine Learning-Based Scoring Function. Physical Chemistry Chemical Physics, 2022, 24, 18291-18305
13. Wray, R.; Wang, J. M.*; Blount, P.*; Iscla, R.* Activation of a Bacterial Mechanosensitive Channel, MscL, Underlies the Membrane Permeabilization of Dual-Targeting Antibacterial Compounds. Antibiotics, 2022, 11(7), 970.
14. Zhong, X.; Choi, J. H.; Hildebrand, S.; Ludwig, S.; Wang, J.; Nair-Gill, E.; Liao, T. C.; Moresco, J. J.; Liu, A.; Quan, J.; Sun, Q.; Zhang, D.; Zhan, X.; Choi, M.; Li, X.; Wang, J. M.; Gallagher, T.; Moresco, E.; M.; Y.; Beutler, B. RNPS1 inhibits excessive tumor necrosis factor/tumor necrosis factor receptor signaling to support hematopoiesis in mice. Proc. Natl. Acad. Sci. 2022, 119(18), e2200128119
15. Orr, A.; Sharif, S.; Wang, J. M.; MacKerell, A. Preserving the Integrity of Empirical Force Fields. Journal of Chemical Information & Modeling, 2022, 62, 16, 3825–3831.
16. Man, V.;* He, X.; Wang, J. M.* Stable cavitation interferes with Aï¢16-22 oligomerization. Journal of Chemical Information & Modeling, 2022, 62, 16, 3885–3895.
17. Wu, J.; Wang, J. M.; Wu, Z.; Zhang, S.; Cao, D.; Hsieh, C. Y.; Hou, T. ALipSol: An attention-driven mixture-of-experts model for lipophilicity and solubility prediction. Journal of Chemical Information & Modeling, 2022, 62, 23, 5975–5987.
18. Yang, S.; Tang, Y.; Liu, Y.; Brown, A. J.; Schaks, M.; Ding, B.; Kramer, D. A.; Mietkowska, M.; Ding, L.; Alekhina, O.; Billadeau, D. D.; Chowdhury, S.; Wang, J. M.; Rottner, K.; Chen, B. Arf GTPase activates the WAVE regulatory complex Q1 through a distinct binding site. Science Advances. 2022, 8, Epub.
Year 2021
1. Guo, X. F.; Wiley, C. A.; Steinman, R. A.; Sheng, Y.; Ji, B. H.; Wang, J. M.; Zhang, L. Y.; Wang, T.; Zenatai, M.; Billiar, T. R.; Wang, Q. D., Aicardi-Goutieres syndrome-associated mutation at ADAR1 gene locus activates innate immune response in mouse brain. Journal of Neuroinflammation 2021, 18 (1), 169.
2. Ji, B. H.; He, X. B.; Zhai, J. C.; Zhang, Y. Z.; Man, V. H.; Wang, J. M.#*, Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction. Briefings in Bioinformatics 2021, 22 (5), bbab054.
3. Ji, B. H.; He, X. B.; Zhang, Y. Z.; Zhai, J. C.; Man, V. H.; Liu, S. H.; Wang, J. M.#*, Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities. Journal of Cheminformatics 2021, 13 (1), 11.
4. Ji, B. H.; Xue, Y.; Xu, Y. Y.; Liu, S. H.; Gough, A. H.; Xie, X. Q.*; Wang, J. M.#*, Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach. ACS Chemical Neuroscience 2021, 12 (10), 1777-1790.
5. Kim, P.; Li, H. Y.; Wang, J. M.; Zhao, Z. M., Landscape of drug-resistance mutations in kinase regulatory hotspots. Briefings in Bioinformatics 2021, 22 (3), bbaa108.
6. Man, V. H.; He, X. B.; Gao, J.; Wang, J. M.#*, Effects of All-Atom Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of PHF6 Peptide of Tau Protein. Journal of Chemical Theory and Computation 2021, 17 (10), 6458-6471.
7. Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J. M.; Nguyen, P. H., Molecular Mechanism of Ultrasound-Induced Structural Defects in Liposomes: A Nonequilibrium Molecular Dynamics Simulation Study. Langmuir 2021, 37 (26), 7945-7954.
8. Man, V. H.; Wang, J. M.; Derreumaux, P.; Nguyen, P. H., Nonequilibrium molecular dynamics simulations of infrared laser-induced dissociation of a tetrameric A beta 42 beta-barrel in a neuronal membrane model. Chemistry and Physics of Lipids 2021, 234, 105030
9. Man, V. H.; Wu, X. W.*; He, X. B.; Xie, X. Q.; Brooks, B. R.*; Wang, J. M.#*, Determination of van der Waals Parameters Using a Double Exponential Potential for Nonbonded Divalent Metal Cations in TIP3P Solvent. Journal of Chemical Theory and Computation 2021, 17 (2), 1086-1097.
10. Su, L. J.; Athamna, M.; Wang, Y.; Wang, J. M.; Freudenberg, M.; Yue, T.; Wang, J. H.; Moresco, E. M. Y.; He, H. M.; Zor, T.; Beutler, B., Sulfatides are endogenous ligands for the TLR4-MD-2 complex. Proceedings of the National Academy of Sciences of the United States of America 2021, 118 (30), e2105316118.
11. Wang, E. C.; Fu, W. T.; Jiang, D. J.; Sun, H. Y.; Wang, J. M.; Zhang, X. J.; Weng, G. Q.; Liu, H.; Tao, P.; Hou, T. J., VAD-MM/GBSA: A Variable Atomic Dielectric MM/GBSA Model for Improved Accuracy in Protein-Ligand Binding Free Energy Calculations. Journal of Chemical Information and Modeling 2021, 61 (6), 2844-2856.
12. Xue, J.; Han, Y.; Baniasadi, H.; Zeng, W. Z.; Pei, J. M.; Grishin, N. V.; Wang, J. M.; Tu, B. P.; Jiang, Y. X., TMEM120A is a coenzyme A-binding membrane protein with structural similarities to ELOVL fatty acid elongase. eLife 2021, 10, e71220.
13. Zhang, Y. Z.; He, X. B.; Zhai, J. C.; Ji, B. H.; Man, V. H.; Wang, J. M.#*, In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor. Briefings in Bioinformatics 2021, 22 (6), bbab188
Year 2020
1. Bogetti, X.; Ghosh, S.; Jarvi, A. G.; Wang, J. M.*; Saxena, S.*, Molecular Dynamics Simulations Based on Newly Developed Force Field Parameters for Cu2+ Spin Labels Provide Insights into Double-Histidine-Based Double Electron-Electron Resonance. Journal of Physical Chemistry B 2020, 124 (14), 2788-2797.
2. Derreumaux, P.; Man, V. H.; Wang, J. M.; Nguyen, P. H., Tau R3-R4 Domain Dimer of the Wild Type and Phosphorylated Ser356 Sequences. I. In Solution by Atomistic Simulations. Journal of Physical Chemistry B 2020, 124 (15), 2975-2983.
3. Ghosh, S.; Casto, J.; Bogetti, X.; Arora, C.; Wang, J. M.*; Saxena, S.*, Orientation and dynamics of Cu2+ based DNA labels from force field parameterized MD elucidates the relationship between EPR distance constraints and DNA backbone distances. Physical Chemistry Chemical Physics 2020, 22 (46), 26707-26719.
4. Hao, D. X.; He, X. B.; Ji, B. H.; Zhang, S. L.; Wang, J. M.#*, How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations? Journal of Chemical Information and Modeling 2020, 60 (12), 6624-6633.
5. He, X. B.; Liu, S. H.; Lee, T. S.; Ji, B. H.; Man, V. H.; York, D. M.; Wang, J. M.#*, Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF. ACS Omega 2020, 5 (9), 4611-4619.
6. He, X. B.; Man, V. H.; Yang, W.*; Lee, T. S.*; Wang, J. M.#*, A fast and high-quality charge model for the next generation general AMBER force field. Journal of Chemical Physics 2020, 153 (11), 114502.
7. Hu, Z. H.; Jing, Y. K.; Xue, Y.; Fan, P. H.; Wang, L. R.; Vanyukov, M.; Kirisci, L.; Wang, J. M.*; Tarter, R. E.*; Xie, X. Q.*, Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity. Drug and Alcohol Dependence 2020, 206.
8. Jarvi, A. G.; Sargun, A.; Bogetti, X.; Wang, J. M.*; Achim, C.*; Saxena, S.*, Development of Cu2+-Based Distance Methods and Force Field Parameters for the Determination of PNA Conformations and Dynamics by EPR and MD Simulations. Journal of Physical Chemistry B 2020, 124 (35), 7544-7556.
9. Ji, B. H.; Liu, S. H.; He, X. B.; Man, V. H.; Xie, X. Q.; Wang, J. M.#*, Prediction of the Binding Affinities and Selectivity for CB1 and CB2 Ligands Using Homology Modeling, Molecular Docking, Molecular Dynamics Simulations, and MM-PBSA Binding Free Energy Calculations. ACS Chemical Neuroscience 2020, 11 (8), 1139-1158.
10. Jing, Y. K.; Hu, Z. H.; Fan, P. H.; Xue, Y.; Wang, L. R.; Tarter, R. E.; Kirisci, L.; Wang, J. M.*; Vanyukov, M.*; Xie, X. Q.*, Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder. Drug and Alcohol Dependence 2020, 206.
11. Kawasaki, T.; Man, V. H.; Sugimoto, Y.; Sugiyama, N.; Yamamoto, H.; Tsukiyama, K.; Wang, J. M.; Derreumaux, P.; Nguyen, P. H., Infrared Laser-Induced Amyloid Fibril Dissociation: A Joint Experimental/Theoretical Study on the GNNQQNY Peptide. Journal of Physical Chemistry B 2020, 124 (29), 6266-6277.
12. Man, V. H.; He, X. B.; Ji, B. H.; Liu, S. H.; Xie, X. Q.; Wang, J. M.#*, Introducing Virtual Oligomerization Inhibition to Identify Potent Inhibitors of A beta Oligomerization. Journal of Chemical Theory and Computation 2020, 16 (6), 3920-3935.
13. Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J. M.; Nguyen, T. T.; Nangia, S.; Nguyen, P. H., Molecular mechanism of ultrasound interaction with a blood brain barrier model. Journal of Chemical Physics 2020, 153 (4), 045104.
14. Wang, E. C.; Liu, H.; Wang, J. M.; Weng, G. Q.; Sun, H. Y.; Wang, Z.; Kang, Y.; Hou, T. J., Development and Evaluation of MM/GBSA Based on a Variable Dielectric GB Model for Predicting Protein-Ligand Binding Affinities. Journal of Chemical Information and Modeling 2020, 60 (11), 5353-5365.
15. Wang, J. M.*, Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study. Journal of Chemical Information and Modeling 2020, 60 (6), 3277-3286.
16. Wei, H. X.; Qi, R. X.; Wang, J. M.; Cieplak, P.; Duan, Y.; Luo, R., Efficient formulation of polarizable Gaussian multipole electrostatics for biomolecular simulations. Journal of Chemical Physics 2020, 153 (11), 114116.
17. Wray, R.; Wang, J. M.*; Iscla, I.*; Blount, P.*, Novel MscL agonists that allow multiple antibiotics cytoplasmic access activate the channel through a common binding site. PLOS One 2020, 15 (1).
18. Xavier, B. M.; Zein, A. A.; Venes, A.; Wang, J. M.; Lee, J. Y., Transmembrane Polar Relay Drives the Allosteric Regulation for ABCG5/G8 Sterol Transporter. International Journal of Molecular Sciences 2020, 21 (22), 8747.
19. Xing, C. R.; Zhuang, Y. W.; Xu, T. H.; Feng, Z. W.; Zhou, X. E.; Chen, M. Z.; Wang, L.; Meng, X.; Xue, Y.; Wang, J. M.; Liu, H.; McGuire, T. F.; Zhao, G. P.; Melcher, K.; Zhang, C.; Xu, H. E.; Xie, X. Q., Cryo-EM Structure of the Human Cannabinoid Receptor CB2-G(i) Signaling Complex. Cell 2020, 180 (4), 645-654.
20. Xue, Y.; Hu, Z. H.; Jing, Y. K.; Wu, H. Y.; Li, X. Y.; Wang, J. M.; Seybert, A.; Xie, X. Q.; Lv, Q. Z., Efficacy assessment of ticagrelor versus clopidogrel in Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention by data mining and machine-learning decision tree approaches. Journal of Clinical Pharmacy and Therapeutics 2020, 45 (5), 1076-1086.
For the full list of the publications, please go to google scholar: https://scholar.google.com/citations?user=VakW0EwAAAAJ
ACTIVE
National Science Foundation/1955260, Wang, Junmei (PI) 07/01/20-06/30/24
Title: CDS&E: D3SC: Developing a molecular mechanics modeling platform (MMMP) for studying molecular interactions
Total Cost: $500,000 Effort: 8.3%
Goals: This proposal seeks to (1) develop and advance a set of computational tools to facilitate users from a broad range of disciplines to generate high quality molecular mechanics force field (MMFF) parameters; and (2) develop a set of MMFF-based models and software tools to facilitate study of molecular interactions with a focus on free energy calculation. The developed software and parameters will be released via the website https://clickff.org, and distributed through AMBER, a mainstream molecular simulation software package.
Role: PI
NIH/ R01GM147673 Wang, Junmei (PI) 09/24/2022-8/31/2026 Title: New generation of general AMBER force field for biomedical research
Total Cost: $1,252,000 Effort: 25%
Goals: We plan to (1) develop a new generation of general AMBER force field (GAFF3) for studying biomolecule-ligand interactions; (2) critically evaluate GAFF3 in protein-ligand and nucleic acid-ligand binding free energy predictions using a novel GPU-accelerated λ-dynamics based orthogonal space tempering (OST) algorithm; and (3) apply a variety of strategies to further improve the performance of GAFF3 until it approaches the best performance an additive force field model can achieve.
Role: PI
NIH/R01 GM149705-01 Wang, Junmei (PI) 04/01/2023-3/31/2028 Title: AI-powered Biased Ligand Design
Total Cost: $1,252,000 Effort: 20%
Goals: Biased ligand design is an attractive approach for designing drugs that target a particular signaling pathway with high specificity and selectivity to minimize side effects, however, it is also a grand challenge due to lack of computational tools. Also, there is an urgent need to expand the druglike chemical space for promising drug targets which have plenty of potent ligands developed, but unfortunately, no approved drugs. We plan to apply artificial intelligence (AI) techniques to overcome the two challenges by developing interaction profile scoring function models to enable biased ligand design and Drug-GAN models to achieve de novo novel chemical structure design.
Role: PI
NIH/R01GM122845 Xie, Lei (PI) 06/01/2023-05/31/2028
Title: AI-Powered Quantitative Systems Pharmacology for AD Drug Repurposing
Total Award Amount: $464,315 (to PITT) Effort: 5%
Major Goals: The major goal of this proposal is to is to develop and experimentally validate innovative machine learning methods for predicting genome-wide protein-ligand interactions and ligand-induced functional activities for drug discovery targeting understudied proteins.
Role: PBPK expert
ACCESS/BIO220051 Wang, Junmei (PI) 6/1/2022-12/31/2022
Title: Discovery of structural and dynamic information of tau oligomerization process through molecular simulations of full-length tau proteins
Total GPU hours: 30,000
Goal: This proposal seeks to elucidate the oligomerization mechanism of full-length tau protein through multiscale molecular dynamics simulations
Role: PI
NIH/K25AG070277 Man, Viet (PI) 8/15/2021-7/31/2026
Title: Development of novel computational protocols to study amyloid oligomerization
Total Cost: $736,240 Effort: 0%
Goals: This career development grant provides support for research on amyloid oligomerization that is associated with neurodegenerative diseases.
Role: Primary Mentor
